Modeling juvenile sea turtle bycatch risk in commercial and recreational fisheries

Summary Understanding the drivers of fisheries bycatch is essential for limiting its impacts on vulnerable species. Here we present a model to estimate the relative magnitude of sea turtle bycatch in major coastal fisheries across the southeastern US based on spatiotemporal variation in fishing effort and the simulated distributions of juvenile Kemp’s ridley (Lepidochelys kempii) and green (Chelonia mydas) sea turtles recruiting from oceanic to nearshore habitats. Over the period modeled (1996–2017), bycatch in recreational fisheries was estimated to be greater than the sum of bycatch that occurred in commercial fisheries that have historically been considered high risks to turtles (e.g., those using trawls, gillnets, and bottom longlines). Prioritizing engagement with recreational anglers to reduce bycatch could be especially beneficial to sea turtle populations. Applying lessons learned from efforts to protect turtles in commercial fisheries may help meet the challenges that arise from the large, diffuse recreational fishing sector.

A process-based model estimated juvenile sea turtle bycatch in coastal fisheries It accounts for variation in ocean conditions, turtle abundance, and fishing effort Model estimates reveal sea turtle bycatch is highest in recreational fisheries Engagement with recreational anglers, in addition to commercial fishers, is needed INTRODUCTION Ontogenetic migration is a common life history trait in marine animals, many of which have an initial dispersive-pelagic oceanic stage followed by recruitment to coastal habitats. 1,2 For species such as sea turtles that have relatively low natural mortality rates on reentering coastal habitats, this period of juvenile recruitment is likely important for determining future reproductive potential of the population; high levels of recruitment may directly translate to more mature adults in the future. [3][4][5][6] Although this ontogenetic migratory behavior allows animals to match environmental conditions with stage-specific physiological requirements, the transition to coastal regions exposes these animals to increased risk from a suite of anthropogenic activities, especially related to fisheries. 1,[7][8][9] The impacts of fisheries bycatch on an individual animal can vary in severity, ranging from mortality to injury and physiological stress. Even sub-lethal effects can reduce the fitness of an individual and, if the bycatch is widespread or of sufficient magnitude locally, can translate to population-level effects. 10 Bycatch of juvenile turtles during the period of coastal recruitment likely limits a population's ability to recover from past anthropogenic stressors. 11,12 In the US, all sea turtle species are protected under the Endangered Species Act, and bycatch of sea turtles is an important driver in the regulation of commercial fisheries. [13][14][15][16] Quantifying changes in bycatch through time is often used to determine whether management actions (e.g., gear modifications, timearea closures, or size limits on catch) are succeeding in reducing anthropogenic risks to turtles. 17,18 However, bycatch is also affected by the abundance and distribution of sea turtles and the amount and overlap with fishing effort: bycatch is more likely in areas where more turtles are present and where more fishing effort occurs. 16,19,20 The number of oceanic-stage juvenile turtles that recruit into a coastal region in any given year may fluctuate widely because younger age classes tend to be abundant, and their movements are strongly influenced by dynamic ocean conditions. 21-23 Annual variability in the abundance and distribution of turtles recruiting to coastal regions can confound the use of fishery-dependent bycatch data to gauge the success of management actions. For instance, is bycatch increasing because mitigative measures are ineffective or because there are more turtles available to be captured? 24 In this paper, we present a mechanistic approach to better understand the drivers of juvenile Kemp's ridley (Lepidochelys kempii) and green (Chelonia mydas) sea turtle bycatch in coastal fisheries across the southeastern US. The model uses spatially explicit fishing effort data and the predicted recruitment of juvenile turtles into those same areas to estimate bycatch. The model does not specify the severity of the bycatch impact (mortality or otherwise), nor does it account for indirect impacts (e.g., lost gear or vessel strikes), rather it identifies the fishing sectors for which the unintentional capture of turtles is most prevalent. This information can contribute to better prioritization of management and engagement programs to reduce turtle interactions in different fisheries.
To model the magnitude of sea turtle bycatch, we assumed that bycatch depends on the spatial overlap between fisheries and turtles and the bycatch rate (catchability) of a particular gear type. To determine the spatial overlap between fisheries and newly recruited juvenile turtles we compiled (1) spatially explicit annual fishing effort data for major coastal fisheries operating in the southeastern US, aggregated by gear type, (2) annual predictions of newly-recruited juvenile turtle abundance within the same spatial areas as the fishing data, and (3) annual observed bycatch within those areas. Further details on these sources of data are available in the STAR Methods. Our approach was to estimate bycatch rates for each fishery as follows: where T b is the number of turtles observed as bycatch in a given area for a given year, T p is the predicted number of turtles from the model in that area for the same year, and F is the amount of gear-specific fishing effort in the area for that year. This assumed that all bycatch in that area, for that fishery, was observed and is thus the minimum bycatch rate for that area. Locations without observed bycatch were not considered. To estimate the total amount of bycatch we computed the geometric mean of all calculated bycatch rates for a given species-gear type ( Table 1). This bycatch rate was multiplied by the total number of predicted turtles in each area multiplied by corresponding fishing effort in each area and summed annually across the southeastern US.

Fishing effort and juvenile turtle abundance
Trends in fishing effort differed by fishery ( Figure 1A). Decreases in effort were seen in commercial offshore shrimping (Pearson's r = À0.90, p < 0.0001), hook and line fishing (Pearson's r = À0.86, p < 0.0001) and bottom longlines (Pearson's r = À0.74, p < 0.0001). An increase in effort was seen in commercial gillnet fisheries (Pearson's r = 0.59, p = 0.004) and in recreational fishing (Pearson's r = 0.59, p = 0.004) ( Figure 1A). Trends in predicted juvenile turtle recruitment differed by species ( Figure 1B). Early in the time series, modeled recruitment was relatively low, but increasing reproductive output in Kemp's ridley and green turtles resulted in predictions reaching more than 1.2 million by 2009. Thereafter, modeled juvenile green turtle abundance continued to grow whereas Kemp's ridley abundance decreased sharply in 2010 and has since leveled off. An overall increase in modeled juvenile recruitment was seen in green turtles (Pearson's r = 0.81, p < 0.0001) but not for Kemp's ridley (Pearson's r = 0.21, p = 0.35).

Relative risk and bycatch
The spatial overlap between juvenile sea turtles and fisheries indicated that the potential for interactions differs among species, fisheries, and regions ( Figure 2). Calculated bycatch rates for Kemp's ridley are two orders of magnitude higher than green turtles in shrimp trawls, gillnets, and recreational fisheries Kemp's ridley 6 3 10 À10 (5 3 10 À11 -5 3 10 À9 ) 5 3 10 À10 (-) 4 3 10 À6 (4 3 10 À7 -4 3 10 À5 ) -9 3 10 À10 (7 3 10 À11 -2 3 10 À8 ) Green turtle 5 3 10 À12 (2 3 10 À12 - iScience Article (Table 1). Although the magnitude of bycatch rates differs between species, the relative risk of different gears/fisheries demonstrate similarities. For instance, gillnets have the highest bycatch rates by 2-4 orders of magnitude, and a km of shrimp trawling has a similar level of risk to a day of a recreational angler fishing (Table 1). Differences in distribution of turtles and spatial variability in the amount of fishing effort, however, result in the amount of predicted bycatch not being directly proportional to these bycatch rates. For Kemp's ridley, the geometric mean of annual bycatch was 2,350 turtles in recreational fisheries, 631 turtles in shrimp trawls, 69 turtles in bottom longlines, 1 turtle in gillnets, and no turtles in commercial hook and line fisheries ( Figure 3A). For green turtles, the geometric mean of annual bycatch was 203 turtles in     Figure 5A). In contrast, our model's predictions for green turtles were substantially less than shrimp trawl bycatch estimates based on the NMFS Observer Program ( Figure 5B). Our model predicted 5 to 35 green turtles, whereas observer-based estimates indicated 227 to 339 green turtles were taken as bycatch.

Risks from recreational fisheries
Among the most important findings of our synthesis is that recreational fisheries have likely supplanted the federally managed shrimping fleet as the fishing sector with the largest amount of bycatch for Kemp's ridley To compute bycatch rates and estimate total bycatch we summed effort from each state across each of these subregions. The mean for the entire period and the maximum/minimum values for any given year  show that effort is heavily weighted to nearshore waters. iScience Article and green sea turtles ( Figure 3). This finding may seem surprising given that all recovery plans and most syntheses have highlighted shrimp trawling as the largest bycatch threat to sea turtles in US waters with little mention of recreational fishing. 14,25-28 Nonetheless, our model's prediction is supported by several independent lines of evidence. First, recreational fishing effort has been increasing whereas shrimping effort has declined ( Figure 1). In addition, managing shrimp trawl bycatch has received considerable attention and includes NMFS-industry engagement (such as through state Sea Grant offices), gear modification (TED development and rigorous testing), an observer program, and electronic logbooks to monitor effort (in the Gulf of Mexico Although not included in our analysis, the same pattern is seen in loggerhead sea turtles (Caretta caretta), where boat strikes in Florida are estimated to kill far more loggerheads per year (712-2,292) 32 than shrimping across the US Gulf and Atlantic coasts (68 individuals per year). 33 Our model supports the conclusions of other studies that indicate bycatch information in small-scale, coastal fisheries (such as those of recreational fishers) are a problematic data gap, as these fisheries may represent a substantial portion of the total anthropogenic interactions and mortality for sea turtles. 15,[34][35][36] The relatively large potential impact of sea turtle bycatch by recreational fisheries is concerning from a conservation perspective because of the challenges associated with managing a diverse and largely independent group of private fishers. 15,30,31 However, there are encouraging indications of NMFS working to improve outcomes for sea turtles caught on recreational charter vessels by requiring boats to carry tools that can more safely remove hooks from sea turtles. 37 In addition, private groups have developed programs to educate recreational fishers in how to minimize negative impacts to turtles, for example, the ''Responsible Pier Initiative'' (https://marinelife.org/conservation/shield/responsible-pier-initiative/). Further stakeholder engagement and research to examine how sea turtle bycatch in recreational fisheries could be reduced and how the rehabilitation of hooked turtles could be improved is recommended. 38,39 Sea turtle ecology and relative bycatch risk Differences in sea turtle ecology are important for determining the relative risk of bycatch. 40 Our modeling indicates that although Kemp's ridley sea turtles are predicted to have lower numerical abundance in the coastal southeastern US than green turtles ( Figure 1B), this species has the higher bycatch rates in recreational fisheries (Table 1) and is at the overall highest risk relative to population size ( Figure 4A). High bycatch rates of Kemp's ridley in recreational fisheries are likely because of the turtles' nearshore distribution where shore-based recreational fishing is concentrated and to a diet that often includes carrion, whereby bait on hooks may be targeted. 41,42 Green turtles also aggregate where recreational anglers are concentrated, such as in seagrass beds and along jetties, but their more herbivorous diet likely puts them at less risk. 43 Modeled bycatch rates in shrimp trawls are higher for Kemp's ridley than green turtles (Table 1). This corresponds well to the relative risks in shrimp trawls reported by others. 14  Hatchling production inputs differ between the species modeled. For Kemp's ridley, we have likely accounted for more than 99% of hatchling production for the species (nesting areas in Tamaulipas, Mexico; Veracruz, Mexico; Texas, USA). 22 For green turtles, annual hatchling production data from potentially important nesting aggregations were unavailable for the years modeled (notably our model does not include the beaches north of Florida, USA; Veracruz, Mexico; Tamaulipas, Mexico; and all nesting areas within the Caribbean, except for Costa Rica). Thus, we expect that our estimates of abundance for Kemp's ridley should be reasonably accurate, but abundance estimates are likely underestimates for green turtles. iScience Article Though the model represents upwards of 90% of the green turtle hatchling production of this region, the missing nesting beaches might be expected to contribute up to several hundred thousand additional juvenile turtles to the coastal US in a year. 44 Regardless, the trends we show in coastal recruits ( Figure 1B) generally track trends in nesting abundance of the major populations of both species 45-47 and thus are informative for assessing relative risk to these species by fishery.
That this model only accounts for the distribution and abundance of young turtles (<3.5 year old) ( Table 4) is likely to give an incomplete picture of bycatch risk. Although there may be indications that the foraging grounds of adult sea turtles could be determined by drift patterns of juveniles 21,48 (which would imply our model might indirectly account for adult distributions), explicitly accounting for the numerical abundance, migrations, and habitat use of adult turtles will be a valuable next step. 49 As noted in the STAR Methods, this model does not include volitional movement by sea turtles to predict distributions but relies on surface currents from an ocean circulation model. The most likely impact of this simplifying assumption is that the abundance of turtles is overestimated in ''lower quality'' areas and underestimated in ''higher quality'' areas, as swimming behavior appears to help turtles target favorable regions. 50,51 Even so, this model appears appropriate for the broadscale questions posed and has utility for contextualizing atypical events with spatiotemporally explicit predictions of juvenile turtle distributions. For example, the model shows a peak in recreational bycatch of Kemp's ridley in 2014 (Figure 4), which resulted from a larger portion of the population moving into the Atlantic Ocean and thus being exposed to the relatively high levels of recreational fishing effort that occur on the US east coast. The caveats noted above likely contribute to the discrepancies between modeled bycatch and estimates based on NMFS Observer Program ( Figure 5). We expect that not accounting for all turtle populations and life-stages would result in underestimates of the magnitude of bycatch. These two issues were most pronounced for green turtles, and the model predicted an order of magnitude less bycatch than was estimated by NMFS for shrimp trawls (Figures 5B and 5C). In contrast, for Kemp's ridley we could account for nearly all iScience Article hatchling production and the age-classes modeled corresponded well the recorded instances of bycatch. The model's predictions closely matched the estimates produced by NMFS for Kemp's ridley ( Figure 5A), suggesting that when basic input data are available this is a robust approach to estimating bycatch.

Conclusions
Our model of sea turtle bycatch provides new information on the potential fishery-related threats encountered by turtles recruiting to the coastal waters along the southeastern US. A cohesive and comprehensive strategy to manage bycatch must account for changes in the physical environment, 58,59 demographic processes that contribute to differing population trends of various species, 60,61 and fluctuations in socioeconomic conditions that influence where and how much fishing effort occurs. 62 Our model is a step toward that goal and the findings presented can be used to prioritize research and management actions among the fisheries and turtle species examined.

STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:

DECLARATION OF INTERESTS
We declare we have no competing interests. iScience Article

Materials availability
This study did not generate new unique reagents.

Data and code availability
All data reported in this paper will be shared by the lead contact upon request.
This paper does not report original code.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Fishing effort
Fishing effort data were obtained for major coastal fisheries operating in the southeastern U.S. and aggregated by gear type. The spatial extent of effort data differed among data sources and gears. Our aim was to compile spatially-explicit, annual fishing effort data in units that allowed comparison among gear types and fisheries relative to the potential for turtle bycatch based on the amount of time that fishing gear is in the water and the spatial extent of fishing ( Figure 1A). All fisheries data spatially overlapped from Texas through North Carolina, but some data extended further north along the eastern U.S. coast. All fisheries data temporally overlapped from 1996 through 2016, but for some fisheries, data were not available for 2017. Data associated with each fishery are described below. For simplicity of presentation, we only report analyses using the total number of trips taken to partition Atlantic shrimping effort rather than those based on the Observer Program's monitored trips. Shrimp trawling effort was provided as the total number of days trawled (24 hours of actual shrimp trawling by one vessel). We multiplied these values by typical trawling speeds, 2.9 knots (128.9 km/day) in the Gulf of Mexico and 2.6 knots (115.6 km/day) in the Atlantic Ocean 63 to account for the increased potential exposure of turtles to shrimp trawls as compared to stationary gear.
Data for commercial hook and line, bottom/demersal longlines, and gillnet fisheries were obtained from NMFS Self-Reported Commercial Coastal Logbook (which includes federally managed fisheries, but not those managed by individual states). These data were available from Texas to Maine (1996-2017). Commercial hook and line fisheries include handline, bandit rigs (electric/hydraulic reels), and trolling (895,336 records), but this category does not include for-hire charter fishing. Bottom/demersal longlines include those targeting sharks, reef fish, and other species (63,448 records). Gillnet fisheries include drift, run, stake, and others (43,256 records). We aggregated effort by trip, year, and statistical zone. In the Gulf of Mexico, statistical zones were the same as described for the shrimping effort ($1 in latitude or longitude along the coastline and extending out to the continental shelf), and in the Atlantic, zones were 1 latitude x 1 longitude. For hook and line fisheries, effort was expressed as the total number of lines reported multiplied by the hours fished (which we converted to days). For bottom/demersal longline fisheries, effort was expressed as the reported average length of a set (in km) multiplied by the reported number of sets and an assumed soak-time of 5 hours (0.2083 days), which is typical in bottom longline fisheries. Gillnet effort was expressed as the reported average length of a set (in km) multiplied by the reported number of sets and the reported soak-time. Recreational fishing effort estimates from MRIP are statistically extrapolated from survey data to provide the annual number of angler trips in a given state (https://www.fisheries.noaa.gov/recreational-fishing-data/recreational-fishing-data-andstatistics-queries). Effort data included both private fishing and for-hire (charter) fishing, the bulk of which use hook and line gear. These data comprised all saltwater fishing (bays, estuaries, sounds, state territorial seas, and extending seaward 200 nautical miles into the federal Exclusive Economic Zone) within each state. Although recreational fishing effort was aggregated over this entire area to accommodate the broad-scale comparisons of the analyses described below, the distribution of effort is strongly weighted to coastal areas (Tables 2 and 3). Our only modification to these data was to add a temporal component to the effort by multiplying the number of angler trips by the average amount of time fishing in a given state. This information is not provided in the online query tool but can be calculated from the database of survey information that is also available. We calculated average recreational fishing duration to be 5.6 hours in Texas, 4 66 For each nesting region, 350 virtual particles were released daily, offshore of the primary nesting sites during each of the 60 d of peak hatchling emergence. This resulted in 21,000 particles released per region annually for 25 turtle cohorts. ICHTHYOP implemented a Runge-Kutta fourth-order time-stepping method whereby particle position was calculated each half-hour as they moved through the HYCOM velocity fields. Virtual particles were tracked for up to 2.5 yr for Kemp's ridley and 3.5 years for green. These drift times are representative of the entire oceanic-stage for Kemp's ridley ($100% of the oceanic stage) and many green turtles ($70-100%).
Owing to little empirical data by which to parameterize the model, no attempt is made to simulate swimming behavior of turtles. Several papers show that including swimming behavior in models can influence predicted distributions and associated metrics of ecological relevance. 50,51,67,68 However, this limitation can be lessened by considering distribution at a relatively broad scale, whereby the influence of fine-scale habitat selection by turtles is reduced and large-scale ocean circulation processes are more important. 44,55 With that in mind, model outputs at 1 latitude by 1 longitude resolution were used as an annual index of juvenile recruitment from the oceanic stage to coastal foraging grounds for the years 1996-2017. Particles that occurred within 1 3 1 bins along the U.S. coast were considered ''coastal recruits'' and the percentage was computed for particles aged 0.5, 1.5, 2.5 and 3.5 yr from each nesting region. For each age class, the percentage of particles was multiplied by the estimate of hatchlings produced at a given nesting region. This value was then multiplied by a daily estimate of oceanic survival based on the median annual estimate (i.e. 81.7%) obtained from the literature. 44 For each species, these values were summed by age class and nesting region to generate an estimate of turtle abundance in each bin, for each available year (Table 4).
Earlier versions of this modeling approach showed to be excellent predictors of the genetic structure at green turtle foraging grounds along the southeastern U.S. coast, 70 correspond well to observed ages of Kemp's ridley recruiting to coastal areas 22,71 and closely match abundance estimates based on in-water data of oceanic-stage green, loggerhead, and Kemp's ridley turtles in the area around the Deepwater Horizon oil spill. 44 The present implementation of this model shows good agreement in predicting spatiotemporal variation in the recruitment of oceanic-stage green and Kemp's ridley turtles to coastal waters in the Gulf of Mexico and along the eastern U.S. coast, as inferred from strandings and survey data. 23,24 The simulations likewise accurately depict stochastic environmental drivers that contribute to annual variation in turtle distributions. 72,73 An important caveat for this model's use here, however, is that hatchling production inputs differ between the species. For Kemp's ridley, we have likely depicted upwards of 99% of hatchling production for the ll OPEN ACCESS iScience 26, 105977, February 17, 2023 iScience Article species (nesting areas in Tamaulipas, Mexico; Veracruz, Mexico; Texas, USA). 22 For green turtles, annual hatchling production data from potentially important nesting aggregations were unavailable for the years modeled (notably our model does not include the beaches north of Florida, USA; Veracruz, Mexico; Tamaulipas, Mexico; and all nesting areas within the Caribbean, except for Costa Rica). Thus, we expect that our estimates of abundance for Kemp's ridley should be reasonably accurate, but abundance estimates are likely underestimates for green turtles. Though we have accounted for upwards of 90% of the green turtle hatchling production of this region, the missing nesting beaches might be expected to contribute up to several hundred thousand additional juvenile turtles to the coastal U.S. in a year. 44 Regardless, the trends we show in coastal recruits generally track trends in nesting abundance of the major populations of each species 45-47 and thus are appropriate for assessing relative risk.

Sea turtle bycatch observations
Bycatch depends on the spatial overlap between fisheries and turtles and the catchability of a particular gear type. Our modeling approach implicitly assumes that catchability is proportional to the number of turtles that are observed as bycatch in each gear type and is equal across effort units and habitats for a fishery. Further data, assumptions, and analyses would be needed to explicitly estimate relative catchability by gear type, habitat, and fishery but is outside the scope of this study. For recreational fisheries bycatch, we obtained 1180 bycatch records (1996-2017) from published sources, which were aggregated by state or sub-region of a state. 38, [74][75][76] For much of the observed bycatch data, the sizes of individuals were unavailable. Thus, it was not possible to parse smaller juvenile turtles (i.e., those recently transitioned from oceanic to coastal habitats) from older juveniles and adults. Given that observed bycatch is necessarily an underestimate of actual bycatch we opted to use all bycatch records available, without respect to turtle size (Table S1).

METHOD DETAILS
To generate bycatch rates for each fishery, we aggregated bycatch annually into 1 3 1 bins that coincided with the turtle distribution and abundance model described above. We then divided the annual amount of bycatch by the product of annual modeled number of turtles and the annual fishing effort in that same bin. To partition shrimping effort and recreational fishing effort into 1 3 1 bins we divided the amount of effort in an area or state by the number of 1 3 1 bins across that part of the coastline (e.g., recreational fishing effort across Texas was divided by 4, whereas recreational fishing effort across Mississippi was not modified). This was conducted for all available instances of bycatch and aggregated by species and gear type. We then computed the geometric mean of all calculated bycatch rates for a given species-gear type ( Table 1). We opted to apply the geometric mean of all bycatch rates to limit the influence of particularly high or low bycatch rates and because there was no temporal trend in bycatch rates for any of the datasets (R 2 < 0.17, p > 0.064, n < 49). We extrapolated the total bycatch by multiplying that rate of bycatch (caught turtles/(available turtles * fishing effort)) across the summed annual risk indices (all available turtles * all fishing effort) for the southeastern U.S. Risk indices are shown for the shrimp fishery (Table S2), recreational fisheries (Table S3), bottom longline fisheries (Table S4), gillnet fisheries (Table S5), and hook and line fisheries (Table S6). Our approach for deriving bycatch rates implicitly assumed that all bycatch within an area was observed, but there is little reason to believe this is the case, and our model might be expected to underestimate actual bycatch. However, our approach also does not account for the many fishing trips that did not catch turtles, which could potentially result in overestimating actual bycatch. While the absolute magnitude of bycatch in a given gear type is uncertain, our model captures a relative amount of bycatch risk among gear types. As such, our model is best suited for examining in which fisheries bycatch risk may be greatest and thus where conservation and management effort might be most beneficial to turtles.